Welding may be used in a variety of manufacturing applications, e.g., fabricating components of a mechanical structure, joining existing components to one another, etc. When a component of a mechanical structure is welded, thermal growth at the weld joint may mechanically distort the parts being joined. Excessive mechanical distortion in a welded component may make it difficult or impossible to assemble the component into the mechanical structure, may have a negative impact on its performance or function, and/or may require additional measures such as post-weld machining to repair the component.
In components with multiple weld joints, the amount of mechanical distortion may depend on the sequence in which the welding operations are performed. For example, mechanical distortion may be reduced by performing the different welding operations in a sequence that balances the forces of distortion. However, determining a welding sequence that generates acceptable amounts of mechanical distortion may be difficult because a component may include several weld joints (and thus a large number of combinatorial sequencing possibilities), the distortion effects may be non-linear and thus hard to predict, and the simulation to predict the distortion may be computationally intensive.
M. H. Kadivar et al., Optimizing Welding Sequence With Genetic Algorithm, 26 Computational Mechanics 514, 514-519 (2000) (“Kadivar”) discloses a genetic algorithm used with a thermomechanical model to determine an optimum welding sequence. The genetic algorithm disclosed in Kadivar chooses sequences designed to minimize distortion and uses a maximum radial displacement parameter as its objective function. The thermomechanical model predicts residual stress and distortion for different populations in the genetic algorithm.
While the genetic algorithm and thermomechanical model disclosed in Kadivar may be used to minimize distortion, it may be computationally intensive in some applications and it does not take other factors into account in the objective function. For example, Kadivar does not incorporate user-generated sequencing constraints, e.g., subsequence constraints, contiguous constraints, rollover constraints, etc., into the genetic algorithm. Instead, by choosing from among all possible sequences, the system in Kadivar may generate unnecessary sequences to apply to the thermomechanical model, thus wasting resources. Further, considering only maximum radial distortion as an objective function limits the applicability of the system in Kadivar.
The disclosed system is directed to overcoming one or more of the problems set forth above and/or other problems of the prior art.